Essays on Income Statement Forecast and Balance Sheet Forecast Case Study

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The paper "Income Statement Forecast and Balance Sheet Forecast" is a perfect example of a finance and accounting case study.   The Income statement forecast i. e. profit and loss can be defined as the main statement for planning financials in business it also shows the businesses financial performance in a certain period i. e. accounting period. However, to forecast the income statement the businesses history must be well understood hence building financial statements. This can, however, be done if only some assumptions are made like growth rate in revenue and expenses, COGS, salaries, rent and utilities, depreciation, taxes, financial modelling tips, interest expense etc.

are all constant for the period of time the forecast will be done thus will not affect the income statement forecast. Forecasting revenue is simply forecasting the growth rates in revenue and we assume that they will remain constant over the years since a change in revenue in a forecasted year will affect the income statement forecast. Growth rates in revenue can be forecast in several ways Macro approach. This is possible when we look at the forecast growth rate of the business thus making adjustments in the business depending on how the business is positioned. Units and prices.

To forecast unit information it can simply be done by redacting revenues from the forecast units and prices. In Forecasting operating expenses (COGS) they tend to have a relationship with revenues i. e. their forecast is a percentage of revenue. However, an exception will be if the business deals with volatile components e. g. fuel and agricultural products. In this case, adjustments are done depending on the forecast for these inputs. In forecasting expense and income we assume that the current expenses of the business and income will not change in the forecasted period since a change in either their expense and income will affect their forecast. Forecasting interest expense will depend on forecast size depending on the businesses assets.

The future capital structure of the business will also affect the forecast of interest expense e. g. in share repurchases, new share issuances thus affecting the income statement forecast. In forecasting depreciation it will depend on forecast size of the businesses total assets, levels of additions to PP& E depending on the rate at which it grows which can be checked at the history of the business thus making adjustments depending on what the business says about future plans.

Can be calculated as follows PP& E = average PP& E / depreciation. Forecasting assumptions for key items of the income statement: 2015 2016   2017 2018 2019 2020 2021 7.05% 11.10% Sales growth rate 6.00% 6.00% 5.00% 5.00% 2.00% 100.00% 100.00% Gross margin 100.00% 100.00% 100.00% 100.00% 100.00% -27.56% -30.56% Depreciation and Amortisation/ PPE -36.00% -36.00% -36.00% -36.00% -36.00% -82.80% -84.35% SG& A/ Sales -90.00% -90.00% -90.00% -88.00% -88.00% -10.10% -9.30% Interest Rate -9.70% -9.70% -9.70% -9.70% -9.70% Forecast income statement (2017 – 2021) Balance sheet forecast Forecast assumption In balance sheet forecast several assumptions are made so as to enhance the business to come up with a balance sheet forecast as calculated below. Assumptions in depreciation, prepaid expenses, accrued expenses, postpaid expenses, sales, dividend etc.

are all constant for the period in the forecast this is because a change in any of them will cause the business to get a wrong forecast in terms of balance sheet forecast. In Forecasting depreciation, we assume that depreciation will remain constant over the years since a change in depreciation will indicate that the forecast will not be applicable in the business.

REFERENCES

Evans, M. K. (2002). Practical business forecasting. John Wiley & Sons.

Hyndman, R. J. (2011). Business forecasting methods. In International Encyclopedia of Statistical Science (pp. 185-187). Springer Berlin Heidelberg.

Lapide, L. (2002). New developments in business forecasting. The Journal of Business Forecasting, 21(2), 11.

O'Connell, R. T., & Koehler, A. B. (2005). Forecasting, time series, and regression: An applied approach (Vol. 4). South-Western Pub.

Zhang, G. P. (Ed.). (2004). Neural networks in business forecasting. IGI Global.

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